AI in Finance Summit New York schedule

•    How AI/ML is revolutionizing customer interactions in financial services through personalized recommendations, AI-driven chatbots, and predictive analytics, creating more engaging and efficient customer experiences?
•    Exploring how AI/ML technologies are improving the accuracy and speed of risk assessments and fraud detection in real-time, helping financial institutions protect assets and maintain trust while reducing operational costs
•    How AI/ML is enabling the creation of new financial products and services

 

 

Motivated by persistent pressures in counter-FinCrime, the session will address synthetic dataset generation typologies and their efficacy in fraud and money laundering use cases, as an area of growing interest. Boosted by new Generative AI capabilities, the option for tailoring datasets to desired use cases for model training, opens new avenues for enhanced coverage of possible fraud landscapes. Simulated datasets promise to overcome the ‘Collective Intelligence conundrum’, that is, the inability to share transaction data for knowledge discovery across networks, on account of PII protection and privacy constraints. The questions now become more acute as to (i) how useful synthetic data can be to train models, as measured for utility, privacy and fidelity, and (ii) what are the limits between generative capability and data representativeness - and how far we can and should go with simulated data.

•    Ensuring that your Training Data is accurate, bias free and trustworthy 
•    Successful practices to tackle bias and improve model outcomes 
•    Promoting accountability for tracking and communicating the lineage of AI-driven outcomes and stress testing models 
•    Applying individually-centric design principles to products and services  

 

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

Panelists:
John Chan, Director of Technology - AI/ML, Raymond James
Sumedha Rai, Data Scientist, New York University
Maurice Leon, Director Service Governance, Information Technology, CIBC Mellon
Harry Mendell, Data Architect Technology Group, Federal Reserve Bank of New York
Vinod Goje, VP Engineering Manager, Bank of America

•    How AI and ML are revolutionizing traditional financial products and services, such as lending, insurance, and wealth management? 
•    Discovering the potential of AI and ML to create new revenue streams and improve profitability for financial institutions 
•    The future directions of AI and ML in financial products, emerging technologies and potential applications 

 

•    What are the key challenges financial institutions face when transitioning from AI hype to practical implementation, and how can they overcome them?
•    How can banks ensure that AI solutions are aligned with both regulatory requirements and business goals to deliver real value?
•    What role does data quality and governance play in the successful adoption of AI in financial services, and what steps should organizations take to improve it?
•    How can financial institutions manage the risks associated with AI, such as biases in algorithms and cybersecurity threats, while still maximizing the technology’s potential?

Panelists:
Sonia Bhargava, Vice President - Software Engineer, Bank of America
Thomas Pellet, Data Science & Technology, Bloomberg 

Innovation Slot 1: 

Innovation Slot 2: 

•    What are the key challenges banks face in keeping up with the rapidly evolving landscape of AI regulations?
•    How can Large Language Models (LLMs) be leveraged to streamline the process of comparing and analyzing AI regulations?
•    What are the specific benefits of using LLMs for regulatory compliance, particularly in terms of efficiency, accuracy, and insights gained?

•    Building a strong foundation for AI: The importance of ensuring data is accurate, complete, and consistent to train reliable AI Models
•    Defining the critical role of data governance in protecting sensitive customer information
•    Outlining the core principles that should guide your organization's data governance efforts
•    Identifying the role of data governance tools and platforms in automating processes and improving data quality

In this keynote, we’ll explore how AI is transforming fraud prevention in the financial and fintech industries. We'll discuss the evolution of fraud threats, traditional methods, and how AI is enhancing fraud detection. While AI is revolutionizing the field, it also introduces challenges like bias and ethical concerns. By the end of the session, you'll gain insights into how AI is reshaping fraud prevention, its opportunities, and the steps businesses must take to stay ahead of emerging threats.

•    Misusing GenAI diminishes the value of AI in organizations
•    Determining whether GenAI makes sense for your use case
•    Considering alternative AI techniques
•    Combining GenAI models with AI other techniques

•    How can we ensure that the development and application of generative AI in quantitative finance aligns with ethical principles and avoids biases?
•    What are the specific data requirements for training and fine-tuning generative AI models in the context of quantitative finance? How can we address data quality and quantity challenges?
•    Given the complexity of generative AI models, how can we enhance their interpretability and explainability to build trust and facilitate regulatory compliance?

 

 

•    Does artificial intelligence increase productivity as expected?
•    What are the biggest challenges financial institutions face when implementing AI solutions?
•    How can human expertise be best combined with AI to create a more robust financial ecosystem?
•    Implementing a Data-Driven approach

•    Evolution of Generative AI in the Workplace - From Personalization to Impersonation: Exploring the shift from AI enhancing personalized experiences to AI performing tasks that mimic human behavior, with cross-industry examples showcasing AI's diverse applications.
•    Human-AI Collaboration - Enhancing Capabilities and Ethical Considerations: How AI complements human work to increase productivity and efficiency, while addressing ethical concerns about AI impersonation and maintaining human interaction.
•    Future of Work- Adapting to and Predicting Change: Strategies for adapting to the evolving AI landscape and potential future scenarios, from utopian collaboration to dystopian job loss.

•    Proving the real value of initiatives as fast as the business expect 
•    Is there a right balance between the emphasis on AI tools, technologies and models, vs ensuring teams spend enough time on measuring incremental value of AI projects? 
•    Balancing short-term wins with long-term success 
•    Key steps towards addressing and improving return on investment

Richa Singh, Director and Head of Data and AI, Lexington Partners 
Aishwarya Ashok, Senior Data Scientist, PURE Insurance

Use cases in Anti Money Laundering, Financial Crimes Compliance
User case in managing Regulatory Development

With the rapid rise of Generative AI (GAI), artificial intelligence has taken center stage for both businesses and regulators alike. As regulations proliferate, staying informed about AI legislative and policy developments across various jurisdictions has become critical. 
Ideally, regulating AI should unlock its potential in a clear, risk-informed manner. But do these regulations achieve that goal? Or do they risk stifling innovation and hindering growth instead?

•    How can we ensure that AI models used in banking are fair, unbiased, and ethically sound?
•    How can we make AI models more transparent and explainable to both internal stakeholders and customers?
•    What are the best practices for communicating the decision-making process of AI systems?
•    How can banks navigate the evolving regulatory landscape for AI and machine learning?

 

•    Identifying how LLMs are transforming the analysis of unstructured financial data
•    How LLMs are enabling the development of innovative fintech product
•    Reviewing the current NLP trends and latest SOTA algorithms Overview of the latest NLP algorithms and industry use cases that are easier to solve using the open-source NLP methods
 

•    Prepare and manage trusted data to unlock the full potential of AI in business operations.
•    Focus on the most valuable AI opportunities that deliver measurable results.
•    Develop skilled teams and robust infrastructures to scale AI across the organization.
•    Embed fairness, explainability, and ethics into AI systems to maintain trust and accountability

•    How open banking is reshaping data access and analytics by providing a more comprehensive view of financial data?
•    How can AI improve the customer experience within open banking? 
•    What can we expect to see from FinTech in the near future and what impacts will this have?